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A Survey on Fingerprint Classification Using Data Mining Algorithms
Abstract
This work presents a Datamining approach for fingerprint classification. The primary aim of datamining is to discover patterns in the data that lead to better understanding of the data generating process and to useful predictions. The major three approaches are: Support Vector Machine, K-Nearest Neighbour and Bayesian classification. In fingerprint classification an automatic approach of identifying the geometric characteristics of ridges based on curves generated by the Orientation field called Orientation Field Flow Curves (OFFC).The above three approaches have been used in fingerprint classification. The SVM has been developed for classification and complex domains. The K-Nearest Neighbour (KNN) decision rule has been a ubiquitous classification tool with good scalability. A given data set can be placed on a firm theoretical foundation using Bayesian Classification. The preliminary findings clearly suggest that they are an effective and promising approach for fingerprint classification. Experimental results show the feasibility and validity of the classification method.
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References
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